Method for Online Monitoring of Mashing Processes Using Infrared Spectroscopy

ABSTRACT

The invention relates to a system and method for controlling an enzymatic pre-treatment process, e.g. a mashing process. The system and method provide for accurate determination of specific sugar molecules as well as the average length of the sugar chains in real-time during e.g. a mashing process. Further information on, e.g., the concentration of dissolved protein and free amino acids can also be obtained simultaneously.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application which claims priorityunder 35 USC § 120 to pending U.S. application Ser. No. 15/303,166,filed Oct. 10, 2016, and thereby claims priority also under 35 USC § 371to PCT application no. PCT/EP2015/057887, filed Apr. 10, 2015, withpriority to Danish (DK) application nos. PA201470208 filed Apr. 11, 2014and PA201570129 filed Mar. 6, 2015, each of which is incorporated byreference herein in its entirety.

TECHNICAL FIELD

The invention relates to a method and a system for controlling anenzymatic pre-treatment process, e.g. a mashing process. This allows foraccurate determination of the sugar molecules present in a solution andof the average length of the sugar chains in real-time during theenzymatic pre-treatment process, e.g. a mashing process. Furthermore,information on e.g. the concentration of dissolved carbohydrates, suchas e.g. protein and free amino acids, may also be obtainedsimultaneously as information on the average length of the sugar chains.

BACKGROUND

When making e.g. a brew, before the microbiological fermentation ofnatural occurring carbohydrates in a wide range of cereals and otherstarch and di- and polysaccharide containing crops, the naturaloccurring carbohydrates often require an enzymatic pretreatment. Thisenzymatic pretreatment is known as mashing. Examples of cereals andother crops that may need to undergo this mashing procedure are barley,wheat, rye, oat, rice, corn, wood or potatoes.

In some cases, the cereals are malted prior to the mashing. During themalting process, natural enzymes are developed within the crops. Inother cases, enzymes are added to the cereals or crops before themashing starts. During the mashing process, water is added to the cropand the temperature is raised and maintained at certain temperatureswhere enzymes added or naturally occurring is most active. A mashingnormally includes several temperature steps to stimulate the differentenzymes.

During a mashing process, different types of mono-, di- and polymericsugar compounds are developed due to the enzymatic transformation of thenaturally occurring starch. Smaller sugar units (such as e.g. maltose)are converted to ethanol during the fermentation whereas the slightlylonger chains (such as, e.g., maltodextrines) are maintained throughoutthe fermentation and adds preferable sweet flavor tones to the finalbrew.

After the mashing process is completed, the ratio of the short-chainedsugars and long-chained sugars must be within a very narrow interval toget the kind of brew wanted. “Over mashing”, where all starches arefully converted into maltose, will yield a brew with a high alcoholcontent but with a flat and unpleasant or bitter flavor. On the otherhand, “under mashing”, where only a minor portion of the starch isconverted to maltose, will result in a very sweet brew with a lowalcohol content. For the most types of brews a mashing process thattakes the mash to somewhere in between these two extremes is preferred.However, the control of the mashing process is very challenging and nogood online methods are available to measure the ratio between shortchain and long chain sugar compounds.

Other important enzymatic processes include hydrolysis of peptide bondsin the proteins of the crop. The content of hydrolyzed protein alsoaffects the characteristics of the final brew and is therefore animportant factor to control in the mashing process. The degree ofhydrolysis of protein is also challenging to control.

The overall brewing process is very well understood from a scientificpoint of view—especially the fermentation process is studied in detailand is often operated as a sophisticated and highly engineeredbiochemical process.

In contrast, the mashing is one of the few “black boxes” left in thebrewing industry and the mashing is still fundamentally performed byusing “recipes” and trial-and-error approaches. This is howeverproblematic in many senses. One concern is that since each batch of maltcan have different compositions with regard to both sugar and proteinsubstrates and active amylase and protease enzymes, the optimal mashingprocedure is never exactly the same.

The reason for the lack of understanding and control of the mashingprocess is because no analytical technologies are available, which offeronline monitoring of the mashing. HPLC (High performance LiquidChromatography) or GPC (gel permeation chromatography) can be used toanalyze the sugar compounds in the mash, but the time to run just onesample is longer than the time scale of each temperature step in themashing itself, thus it is useless for online monitoring.

Hence, new analytical technologies are needed to optimize and allowbetter understanding of this important biochemical process type.

DESCRIPTION OF THE INVENTION

Disclosed herein in a first aspect of the invention is a method forcontrolling an enzymatic pre-treatment process, e.g. a mashing process.The method comprises the actions of:

-   -   a) providing a sample to a system with a tank;    -   b) obtaining a sample mixture by:        -   adding one or more enzymes to the sample if the sample does            not contain one or more enzymes already, or        -   possibly adding one or more enzymes to the sample already            containing one or more enzymes;    -   c) continuously exposing a part of the sample mixture to an        infrared (IR) spectrometer;    -   d) continuously measuring attenuated total reflectance (ATR) IR        spectra of the sample mixture with the IR spectrometer in real        time at wavelengths between 400-3500 cm⁻¹ during the enzymatic        pre-treatment process, and    -   e) feeding the measured IR spectra to a calculating unit which:        -   calculates information relating to specific species present            in the sample mixture during the enzymatic pre-treatment            process based on the IR spectra, wherein the information            relating to the specific species present in the samples            mixture is:            -   the ratio between the different specific species, and/or            -   the concentration of one or more of the specific                species, and/or            -   the degree of polymerization of one or more of the                specific species; and        -   feeds the information relating to the specific species in            the sample mixture back to the user and/or to a tank control            system connected to the tank.

Disclosed herein in a second aspect of the invention is a system forcontrolling an enzymatic pre-treatment process. The system comprises atank adapted for containing a sample mixture comprising a sample to beenzymatically pre-treated and possibly one or more enzymes added to thesample during the enzymatic pre-treatment process.

The system further comprises an analyzing unit connected to an IRspectrometer, the analyzing unit being adapted for bringing the samplemixture in direct contact with the IR spectrometer for measuringattenuated total reflectance (ATR) IR spectra of the sample mixtureduring the enzymatic pre-treatment process.

The system also comprises a calculation unit connected with the IRspectrometer, the calculation unit being adapted for calculating:

-   -   the ratio between specific species in the sample mixture based        on the IR spectra of the sample, and/or    -   the concentration of one or more specific species based on the        IR spectra of the sample, and/or    -   the degree of polymerization of one or more of specific species        based on the IR spectra of the sample.

By the first and second aspect of the invention is thereby obtained amethod and a system that can accurately determine the specific sugarmolecules present in the solution and the average length of the sugarchains in real-time during e.g. a mashing process. Further informationon e.g. the concentration of dissolved protein and free amino acids canalso be obtained simultaneously.

The pre-treatment of crops prior to their fermentation in the process ofmaking commercial grade ethanol, e.g. for the use as fuel, solvents oradditives, is a process virtually identical to the mashing process usedin the production of beverages or distilled liquors for humanconsumption as described above.

The feedstock for the fermentation process for making ethanol mayinclude the same starch-containing crops as mentioned above for themashing process, but also non-eatable crops or agricultural waste. A fewnon-limiting examples could be, willow, wood, bagasse or corn stover. Inthese types of crops, cellulose is a significant part of the sugarcompound. Cellulose is a compound with a chemical structure almostidentical to starch, where the conformation of the 1,4-glycoside bonds,which binds the glucose together, is oriented slightly different, givingthe dissolved cellulose, cellulose oligomers and the analogous dimercellobiose almost equal chemical and spectroscopic properties. Hence,the term “mashing” is also to be understood as including the similarenzymatic pretreatment of a starch or lignocellulose containingfeedstock for fermentation in the production of commercial gradeethanol.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1, 2, 3 a-b and 6 a show embodiments of the mashing unitintegrated with an IR spectrometer and a computer.

FIGS. 4 and 5 show an IR spectrometer from the outside and the inside(FIG. 4 and FIG. 5, respectively).

FIGS. 6b-d show embodiments of the mashing unit.

FIGS. 7a-d show embodiments of extraction and/or recycling probes.

FIGS. 8a-b show a spectroscopic unit, where FIG. 8b is an exploded view.

FIGS. 9a-c show a close-up of a spectrophotometer and an ATR (AttenuatedTotal Reflectance) unit.

FIGS. 10a-b show a close-up of a spectrophotometer and an ATR unit andFIG. 10c shows a close-up of a spectrophotometer combined with only acrystal.

FIGS. 11a-b show a first embodiment where the spectroscopic unit ofFIGS. 8a-b is connected with a tank and FIGS. 11c-d show a secondembodiment where the spectroscopic unit of FIGS. 8a-b is connected witha tank.

FIG. 12 shows the IR spectra of glucose, maltose, maltotriose andmaltodextrin.

FIG. 13 shows the characteristic vibrations of the glycoside bond andthe acetal group in the starch related di- and polymers- of glucose, aswell as the related modes in the monomeric glucose analog.

FIG. 14 shows the integrated absorbance of the band at 1026 cm⁻¹ as afunction of glycoside bonds per glucose unit for the IR spectra ofglucose, maltose, maltotriose and maltodextrin.

FIG. 15 shows IR spectra measured during the mashing process of starch.

FIG. 16 shows the relationship of integrated IR absorption from 1260-950cm⁻¹ and the Brix % during mashing of malted barley.

FIG. 17 shows the IR spectra of the same sample aliquot during mashing,with and without filtration.

FIG. 18 shows the mechanism behind this phenomenon observed in FIG. 17.

FIG. 19 illustrates the hydrolysis of the peptide bond.

FIG. 20 shows the hydrolysis of amylo-starch.

FIG. 21a shows the conversion of the pure anomeric α-glycosyranose formthe racemic mixture at Room temperature.

FIG. 21b shows the comparison of the pure dissolved α-D-glucopyranosewith racemic mixture of α and β-forms D-glucopyranose at equilibrium.

FIG. 22 shows the IR spectra following the in situ conversion ofα-Glycopyranose.

FIG. 23 shows the spectra of four different carbohydrates and thedeconvolution of each spectrum.

FIG. 24 shows the plot of several of the deconvoluted band areas of FIG.23 against the RDP values obtained from the DE values from the Visspectroscopic Cu(II)-Cu(I)-2,2-bquinoline-carboxylate.

FIG. 25 shows the spectra of different α-(1,4) glycoside bond containingcompounds.

FIG. 26 shows the IR spectra of a calibration set.

FIGS. 27-28 show correlations between deconvoluted band areas of spectrain FIG. 26 and the relative degree of polymerization at differentwavelengths.

FIG. 29 shows the IR spectra of glucose and maltodextrin.

FIGS. 30-33 show correlations between deconvoluted band areas of spectrain FIG. 29 and the relative degree of polymerization at differentwavelengths.

FIG. 34 shows ATR-IR spectra of the different samples.

FIG. 35 shows the analysis of IR spectra measured in real time during amashing process.

DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention uses Mid infrared (MIR) spectrometers. Theseinstruments operate in the Mid IR region, which is typically defined asbeing from 400 up to 4000 cm⁻¹. The absorptions of Mid-infrared bringsthe molecular bond in the irradiated sample to vibrate either throughbending or stretching deformations. Each vibration type corresponds tothe absorption of infrared light of one specific wavelength. The sum ofall these vibrations and the corresponding absorptions of mid infraredphotons at the respective wavelength results in a Mid-infrared spectra.

The position and intensity of each photon absorption and thecorresponding vibration obey the rules of quantum mechanics, and cantherefore be predicted and theorized using different approximations andmodels. Due to these mechanisms behind the mid infrared spectroscopy, novibration in practice exceeds 4000 cm⁻¹. Also, for the purpose of thisinvention the interesting part of the spectra lies far below 3600 cm⁻¹.

The true infrared spectrometers used in this invention are oftenconfused with Near infrared spectrometers, which despite the similarname are very different. Near infrared NIR spectrometers operates abovethe MIR wavenumbers, typically up to around 10000 cm⁻¹, or in somespectrometers all the way up to visible or UV wavelengths. None of theprimary “true” vibrations are found in the Near infrared spectra, onlyovertones and combination bands of the actuals vibrations. The overtonesare typically scrambled and much harder to use analytically and mostoften very different technologies are applied for the spectrometersthemselves compared to MIR.

In the following description of the invention, the term IR will referexclusively to Mid IR spectroscopy, and should not be confused with NearIR spectroscopy. It is very important for the present invention that itapplies only to MIR and not to NIR, i.e. the two types of infraredspectroscopy are not to be confused.

The possibility to use infrared spectroscopy to analyze samples relatedto brewing has been evaluated in previous work for a laboratoryenvironment for MIR as well as NIR (J. Tehnhunen et al., J. Inst Brew.volume 100 (1994) pages 11-15). The article measures samples of glucose,maltose and maltotriose and finds that especially ATR-FTIR is unsuitableto use in the real mashing/brewing analysis. Hence, the article focuseson NIR and transmission FTIR. The results of the works clearly showsthat NIR is not useful in discriminating the components in theirsamples, and the errors are of significant larger magnitude than thefactors the authors tries to identify in the samples. Transmission MIRis not technically relevant in the process as the highly toxic windowmaterials that allows transmission in the fingerprint region, in thetransmission cell will dissolve in the sample liquid. Further, the cellused has so narrow a pathlength that it would be technically impossibleto use in a real brewing process.

In U.S. Pat. No. 4,913,914, a method to monitor the “maromi mashing” isproposed (for e.g. Soya Sauce production). “Maromi mashing” is a longfermentation process of soybean and rice that lasts from months toaround a year, using koji-mold and lactic acid bacteria. Despite thelinguistic similarity, the fermentation process of “maromi mashing” isvery different from the mashing processes described in the presentinvention; as relatively fast enzymatic pretreatment. The patentdescribes the use of a near infrared spectrometer and an auto samplersystem to monitor the mash fermentation process; not a true in-lineanalyzer. The defined NIR spectrometer works in an entirely differentwavelength, where none of the molecular vibration described in thepresent invention can be found. As described in the background on MIRand NIR, it is essential to understand the difference between MIR andNIR spectroscopy. The discrimination of mono-, di- and poly-saccharideswhich is essential in the present invention is not mentioned.

DE4002108 (A1) discloses an automation of a well-known destructivemethod in starch analysis, where the starch helix structure can beidentified by addition of iodine. The purple complex between amylasehelix and iodine can then be quantified using a NIR-VIS spectrometer.The process consumes iodine and the sample cannot be returned to themashing unit. It basically describes the automation of a well-knownanalysis, applying VIS-NIR spectroscopy in a another wavelength intervalthan the present invention.

WO2009/074650 discloses a method where unmalted cereals in combinationwith added enzymes, mainly amylases, is used as a substitution of maltedbarley and malted cereals in brewing. The invention describes mechanismsof the mashing or brewing that are well known to any person skilled inthe art of brewing, but does not describe, in any terms, how to useinfrared spectroscopy to monitor the process.

EP0851027A1 discloses a method using ATR-FTIR spectroscopy to controllactic acid bacteria in fermentation processes. The invention describeshow to control lactic acid bacteria, through monitoring the two forms oflactic acid (protonated and deprotonated), and how they can bequantified using ATR, making the method useful at different pH values.The process also describes how to control lactic acid bacteria throughthe intensity of “alcoholic signals” from glucose. However, thediscrimination of mono- and polysaccharides is not mentioned. Thequantification of glucose using ATR-FTIR alone is straight forward andnot surprising in itself. The method does not describe an apparatus forthe analysis in-line, or the use of ATR-FTIR to control an enzymaticpre-treatment.

The present invention comprises a methodology where attenuated totalreflectance (ATR) infrared (IR) spectroscopy is applied in real-time tomonitor enzymatic pre-treatment processes such as e.g. mashingprocesses. By ATR-IR spectroscopy is meant spectroscopy, where the IRspectra between 400-3500 cm⁻¹ is measured. Simultaneously with themeasurements of the IR spectra, a computer calculates an accuratecomposition of the components in the solution, i.e. the mash, andreturns biochemical key values to the operator or to a control system,which overall allows for a better control and optimization of themashing procedure. Examples of such important key values are the ratiobetween mono, dimeric and polymeric sugar compounds, the totalconcentration of dissolved sugars, and the concentration of proteins orimportant flavoring compounds. Further parameters may also be monitored.

In one or more embodiments the method comprises the step of stopping theenzymatic pre-treatment process when a predetermined ratio between thespecific species in the sample mixture is obtained, and/or theconcentration of one or more of the specific species reached apredetermined level, and/or the degree of polymerization of one or moreof the specific species reached a predetermined level. The enzymaticpre-treatment process may either stopped manually by the user orautomatically by the system on basis of the information provided fromthe calculation unit.

In one or more embodiments, the sample mixture is stirred during atleast part of the enzymatic pre-treatment process.

In one or more embodiments, water is added to the sample mixture duringthe enzymatic pre-treatment process, e.g. together with the possibleaddition of the one or more enzymes.

In one or more embodiments, the temperature in the sample and/or samplemixture is increased and/or is decreased either prior to starting theenzymatic pre-treatment process, during the enzymatic pre-treatmentprocess, and/or in order to stop the enzymatic pre-treatment process.

In one or more embodiments, the enzymatic pre-treatment process isstopped by the system opening the tank automatically, or alternativelyby removing the sample mixture from the tank. Also, the enzymaticpre-treatment may be stopped by increasing the temperature in the tank.The enzymatic pre-treatment process is normally stopped when thepredetermined ratio between the specific species in the sample mixtureis obtained, and/or when the concentration of one or more of thespecific species reaches a predetermined level, and/or when the degreeof polymerization of one or more of the specific species reaches apredetermined level.

In one or more embodiments, the sample is selected from naturaloccurring carbohydrates in e.g. cereals and other starch and di- andpolysaccharide containing crops such as e.g. barley, wheat, rye, oat,corn, rice, potatoes, straw, wood, starch and corn stover.

In one or more embodiments, the enzymatic pre-treatment process is amashing process conducted prior to a fermentation process such asbrewing.

In one or more embodiments, multiple enzymes are added to the samplemixture either at the same time or at different times and wherein thetemperature of the sample mixture is adjusted during the pre-treatmentprocess to account for different temperature levels at which each of themultiple of enzymes are most active.

In one or more embodiments, the IR spectra are measured at wavelengthsbetween 400-3000 cm⁻¹, between 400-2000 cm⁻¹, between 500-1500 cm⁻¹,between 700-1400 cm⁻¹, or between 800-1300 cm⁻¹.

In one or more embodiments, the analyzing unit is an ATR-IR cell adaptedfor containing a small part of the sample mixture during measurements ofATR-IR spectra of the sample mixture during the enzymatic pre-treatmentprocess, wherein the ATR-IR cell is mounted tightly to an ATR-IR platecomprising a crystal, the ATR-IR plate being part of an ATR-IRspectrometer, where the tight mounting is obtained by means of a clampon the ATR-IR spectrometer and an O-ring positioned between ATR-IR plateand the analyzing unit.

In one or more embodiments, the system further comprises a connectionunit connecting the tank and the analyzing unit, the connection unitbeing adapted for guiding a small part of the sample mixture from thetank to the analyzing unit whereby ATR-IR spectra of the sample mixtureis measured by the ATR-IR spectrometer.

In one or more embodiments, the system comprises an extraction probewhich protrudes inside the tank for extracting a small sample part forthe IR spectra measurement at a user-determined position inside thetank.

In one or more embodiments, the analyzing unit is a spectroscopic unitcomprising an ATR-IR unit, a spectrophotometer for measuring IR spectra,and a computer, wherein the ART-IR unit comprises an ATR-IR plate with acrystal.

In one or more embodiments, the spectroscopic unit is attached directlyto a wall of the tank.

In one or more embodiments, the spectroscopic unit is connected to thewall of the tank by connecting means e.g. in the form of a set of hoses.

In one or more embodiments, the ATR plate can be turned up to 90 degreesaround its own axis.

FIG. 1 shows a setup comprising a mashing unit 100 with a solution 102containing products to be mashed and possibly the enzymaticsolution/compounds which are to be added into the feedstock products. Inthe embodiment shown in FIG. 1, an ATR-FTIR (Attenuated Totalreflectance Fourier Transform Infrared) probe using a waveguideprinciple 106, is inserted directly into the mashing unit 100. The IRspectra are collected and sent to a computer unit 110, whichdeconvolutes each IR spectrum. The area of bands characteristic to thetotal sugar/starch content as well as the bands specific to thepolymeric sugar compounds can be extracted from the spectra. Therelevant band areas are then used to calculate the concentration ofindividual polymeric sugar compounds and a ratio between monomeric andpolymeric sugar compounds using premade calibration curves. Furthervalues like protein content, and free amino acid content may also bereported simultaneously.

The mashing unit 100 also comprises a stirring unit 104, which may be apropeller or similar as illustrated in FIG. 1. An infrared (IR)spectroscopy measurement apparatus 200, e.g. an Attenuated TotalReflectance (ATR) IR spectrometer, is coupled directly to the mashingunit 100 via an optical connection path 106, e.g. a setup comprisingmirrors or similar optical components for guiding the IR light from theIR spectrometer 200 to the sample 102 and for guiding the back-reflectedlight from the sample 102 and to the IR spectrometer 200.

The IR spectrometer 200 is in turn connected to a computer 110 orsimilar data processing device, which calculates the individual sugarcompounds and the ratio between monomeric and polymeric sugarcompounds—possibly along with the protein and/or free amino acidcontent—in real time.

In another embodiment as shown in FIG. 2, a fiber 116 optical FTIR probe114 is inserted into a side channel 108 of the mashing tank 100 where itcollects IR spectra continuously. In a variation of this embodiment, thecomputer 110 is integrated into the cabinet of the spectrometer 200,which performs multivariate data analysis on each spectrum and shows therelevant values directly on a display 112 on the spectrometer 200.

In an embodiment similar to the one shown in FIG. 1, an FTIRspectrometer 200 is equipped with an ATR-cell which is mounted directlyon the mashing tank 100. In this way, the product 102 being mashed andan ATR crystal in the ATR-cell interact intimately through a hole 118 inthe tank 100. The position of the IR spectrometer 200 on the tank 100can be either on the side or at the bottom of the tank as shown in FIG.3a and FIG. 3b , respectively.

A valve may be used for allowing the extraction of sample 102, i.e. themash, from the mashing tank 100. By using either a pumping unit, thepressure in the inside the mashing unit 100 or gravity, a sample may besent to the IR spectrometer 200, e.g. a Fourier Transform (FT) IRspectrometer, positioned adjacent to the mashing tank 100. The FTIRspectrometer equipped with a customized ATR-Unit can thereby measure andrecord IR spectra of the solution 102 being mashed. Such sampleextraction may be operated either as a continuously flow, or samplealiquots may be extracted at preferred times during the mashing process.A specific embodiment of such a customized ATR unit utilizing anATR-cell and a special add-on analyzing chamber is shown in FIGS. 4 and5.

FIG. 4 shows the IR spectrometer 200 seen from ‘outside’ and FIG. 5shows a view ‘inside’ part of the spectrometer 200 and an analyzingchamber 300 where the sample is lead to. The IR spectrometer comprises abox 202 where the IR light is produced and an ART-IR unit 203. Theincoming IR light 204 is guided via a set of optical components 206,e.g. mirrors, to a crystal 207 for obtaining intimate contact with thesample inside the analyzing chamber 300. The reflected IR light 210 islikewise guided away from the crystal 207 to a unit inside thespectrometer 200 for analyzing the spectra.

The analyzing chamber 300 may e.g. be an ATR-IR cell possibly being anadd-on device and is normally secured to the spectrometer 200 by a clamp208 normally being an integrated part of the spectrometer 200. A tightsealing between the analyzing chamber 300 and the spectrometer 200 isnormally obtained by using an O-ring 212, which ensures that the liquidsample stays inside the analyzing chamber 300. The sample is guided intothe analyzing chamber 300 by a connector inlet 302 and guided away againby a connector outlet 304. The connector inlet and outlets 302, 304 maybe releasable connected to the analyzing chamber 300.

In one or more embodiments, an analyzing chamber 300 is builtpermanently together with the ATR-unit 203 by the manufacturer. In otherembodiments, the ATR-unit 203, analyzing chamber 300 and spectrometer200 are all fully integrated into one combined unit.

After each IR spectrum measured by the spectrometer 200, the sample inthe analyzing chamber 300 is discarded as waste or returned into themashing tank 100.

The IR spectra are analyzed by customized software that extracts bandareas from the collected spectra and converts them to relevant values,such as sugar contents, average length of sugar polymers, dissolvedprotein content and its degree of hydrolysis, concentration of flavoringcompounds such as diacetyl, ester compounds or bitter compounds etc.

An advantage of the spectrometer 200 combined with the analyzing chamber300 described above is that it may be calibrated independently of theoperation of the mashing tank 100.

In FIG. 6a it is illustrated that the tank 100 may have extraction andrecirculation valves 120 at the inner wall of the mashing tank 100 and awaste tank 122 for through a valve disposing samples after the IRspectrum of the sample has been measured. The setup may also be equippedwith a pump unit 124 for recycling the sample 102.

In one or more embodiments, extraction 126 and/or recirculation probes127 may be positioned close to or in connection with the valves 120 asis illustrated in FIGS. 6b-6d . Some none limiting examples ofextraction probes 126 are shown in FIGS. 7a -d.

In one or more embodiments, an automatic cleaning system may be used toclean the analyzing chamber 300 when the mashing tank 100 is notoperating. The automatic cleaning system may function by pumping a flowof cleaning agents or chemical such as bases, acids or organic solventstrough the analyzing chamber 300, and rinsing with water. Calibrationstandards may further automatically be pumped into the analyzing chamber300 when the mashing tank 100 is not operating to run a calibrationprogram.

The inlet 128 (and possible outlet 130 as shown in FIG. 7a ) in theextraction probes 126 is dimensioned to allow large particles to flowfreely through the probe 126 without plugging the system. In one or moreembodiments the extraction probe 126 is fitted with a filter 132 toconstrain particles too large for the rest of the system. In anothervariation, the probe is designed with a removable filter so the masksize can be changed. A filter may also be omitted as shown in FIG. 7 c.

In one or more embodiments, a part 134 of the extraction probe 126extends into the mashing unit 100 to extract the liquid sample from adesired location in the mashing tank 100. The extraction probe 126 willnormally be connected to the tank 100 by a vessel mount 136.

In one or more embodiments, the extraction probe 126 may act as a valveon the inner wall of the mashing tank (a non-limiting example is shownon FIG. 7d ).

The extraction probe 126 shown in different versions in FIG. 7a-c mayextract samples from different locations in the mashing tank 100. Thismay be done by using a series of valves connected to a tube inside themashing tank 100. In yet another variation the extraction probe 126 mayuse motors to extend and change location inside the mashing tank 100.

As shown in FIG. 7a , the probe may comprise of two individual probes:an extraction probe 126 and a recirculation probe 127, where one is forinlet 128 of the sample and one for outlet 130 to allow flow back intothe mashing tank 100. By using a pump applied to the system, thedirection of the flow in the system may be changed to prevent clumpingin the filters. In this way, the two individual probes (extraction probeand recirculation probe) may be combined to a double probe handling bothin- and outlet.

The extraction probe 126 may be inserted into the mashing tank 100 usinga mount inside de mashing tank 100. The mashing tank mount allows easyinstallation and maintenance of the probe 126. The mount may utilizetri-clamp connections to connect a tight seal between the probe 126 andthe mashing tank 100.

The mount seals may also be created by using a vacuum between the probe126 and the mashing tank 100. Alternatively, the mount may be weldedonto the wall of the mashing tank 100.

FIG. 8a show an IR spectroscopic unit/analyzing unit 400 according tothe invention and FIG. 8b shows the spectroscopic unit 400 in anexploded view. The spectroscopic unit 400 comprises a spectroscopicenclosure 402 with a plate 404 in which a crystal 407 is mounted, Thecrystal 407 must be in contact with the media in order for thespectroscopic unit 400 to perform IR measurements. An IR spectroscopicconstellation is used to direct Infrared light through the crystal 407.In the embodiment shown in FIGS. 8a-b this is done by using aspectroscopic unit 400 with a spectrophotometer 406 and an ATR-IR unit403 consisting of either mirrors or optics to direct the light from anemitter in the spectrophotometer 406 through the crystal 407 and backinto a receiver in the spectrophotometer 406.

The emitter may receive IR light from an external light source by meansof guiding optics and/or mirrors or fibers. Alternatively, thespectrophotometer 406 may contain a diode array emitting IR light insidethe spectrophotometer 406. The receiver may likewise guide the backreflected light to an external spectrometer or in itself contain diodereceivers.

In an embodiment, the crystal 407 will only cause the light to bouncethrough the media once (single bounce) as shown in FIG. 10a , while inanother embodiment the crystal 407 will cause several bounces throughthe media (multi bounce) as shown in FIG. 10 b.

In another embodiment the ATR unit 406 is neglected and the emitter andreceiver are angled towards the crystal 407 creating a direct path toand from the crystal 407 as shown in FIG. 10 c.

In another embodiment the receiver is replaced by a photo sensor andfilter.

The spectroscopic unit 400 further comprises a computer 408 connected tothe spectrophotometer 406 for performing the necessary data treatmentbefore the data is sent back to the user or to a central control system,e.g. a tank control system connected to the tank. The computer 408 maybe connected to an antenna 410 for wireless communication. Alternativelyor as a supplement, the computer 408 may be connected to a display 409for direct interface with the user. The spectroscopic unit 400 alsocomprises a lid 411.

In order to perform spectroscopic measurements on a media, thespectroscopic crystal 407 must be in contact with the media. This can bedone e.g. as in-line measurement, where the spectroscopic unit 400 ismounted directly on a tank, vessel or pipe.

FIGS. 11a-b shows an embodiment of an in-line system comprising a tank,e.g. a mashing unit 100 with the IR spectroscopic unit 400 attacheddirectly to the side of the mashing unit 100. The spectroscopic unit 400is mounted to the mashing tank 100 using a sealed mount unit 401 to keepthe crystal 407 in contact with media while preventing any leakage. Thesealed mount unit 401 comprises a tank mount 412, a clamp 416 totightening the connection between the spectroscopic unit 400 and tankmount 412 and a seal 414 for sealing the connection between thespectroscopic unit 400 and the tank mount 412.

The mount 412 may be placed on the side of a vessel/tank 100 as shown inFIG. 11a , on the bottom or top of the vessel/tank 100.

When sample residue is left on the crystal 407 when the spectroscopicunit 400 is not in use, the residue solidifies and the ATR unit 403 mustbe cleaned prior to new measurements. In an embodiment shown in FIGS.9a-c , the surface, in which the crystal 407 is embedded, is tilted toallow the sample to drain off the crystal 407. In FIGS. 9a-c , a 45degree tilt is shown in FIG. 9b compared to FIG. 9a and a 90 degree tiltis shown in FIG. 9 c.

In one embodiment, the entire spectroscopic unit is drained to allowdrainage. In another embodiment, a tilting base is implemented betweenthe spectrometer and the ATR unit allowing the ATR unit to tilt alongthe axis of the IR beam. In an embodiment, the tilting base isadjustable to tilt to a desired degree. In an embodiment the tiltingbase is motorized allowing it to tilt additionally between measurements.

Another method for keeping the crystal 407 in contact with the media isusing an at-line setup as shown in FIGS. 11c-d . In this embodiment, thespectroscopic unit 400 is mounted to a pumping unit 500 and connected toa filtration unit mounted on the tank 100 via hoses 602. The two hoses602 may be replaced by pipes.

The filtration unit 600 may be a simple filter screen. Alternatively,the filtration unit 600 may be replaced by a tank mount 412 as shown inFIGS. 11a-b . Yet alternatively, the filtration unit 600 may consists ofprobes reaching into the tank or vessel 100.

The pumping unit 500 comprises a peristaltic pump system with a housing501, a motor 502, a pump head 504 utilizing media displacement to movethe media to and from the vessel 100, and a flow cell 506 for directingmedia across the crystal 407. The flow cell 506 is connected to the pump504 to ensure proper flow across the surface of the crystal 407. Thepumping unit 500 also comprises a seal 414 and a clamp 416 for obtaininga tight seal between the spectroscopic unit 400 and the pumping unit500.

In an embodiment, the pumping unit 500 is placed on the floor. Inanother embodiment the pumping unit 500 is mounted to a wall or thevessel itself.

The invention may be explained through the following non-limitingexamples:

Example 1

Aqueous solutions of respectively 5% w/w of glucose, maltose,maltotriose and maltodextrin are prepared and IR spectra of all thesolutions are recorded. These spectra can be found on FIG. 12. Thebottom line in FIG. 12 shows the spectrum of pure water for comparison.The IR spectra are originating from vibrational modes in each compound.The relatively large change in the infrared spectra of the fourcompounds that are almost chemical identical, shows that the changesmostly originates from the polymeric 1,4-glycoside bond. FIGS. 23 and 25further shows measurements of the same species as shown in FIG. 12.

The glycoside bond itself has a characteristic antisymmetric C—O—Cstretching mode that gives a band at around 1160 cm⁻¹. However due tothe overlap of the C4-O stretching in glucose and the terminal C4-OHgroups on the polymers, it is not suitable to distinguish betweenmonomeric and polymeric species.

However the acetal group, which is a consequence of the glycoside bond,induces more significant difference in the spectra seen by thepronounced decrease of intensity of the band at 1026 cm⁻¹ upondepolymerizing—see illustration of this process in FIG. 13. Hence thedisappearance of the 1026 cm⁻¹ band is a good indicator fordepolymerization.

Like other forms of transmission spectroscopy Lambert Beers Law appliesto ATR-FTIR spectra, the law reading:

A=ε*I _(penetration) *c  [1]

where A is the absorption, ε is the extinction coefficient,I_(penetration) is the penetration depth of the infrared beam, and c isthe concentration of the compound causing the absorption.

The penetration depth is constant when using ATR-FTIR for samples withsimilar refractive index, which means that the height or the area ofeach band is proportional with the concentration of the compound thatcauses the band.

The characteristic vibrations of the acetal group is so characteristicfor the polymeric band, that it can be used for accurate quantificationof the total glycosides bands per glucose unit in the mixture ofdifferent sugar lengths. One way of measuring the abortion could be justsubtracting the spectrum of pure water from each spectrum and readingthe peak height at around 1020-1030 cm⁻¹, where vibrations from theacetal group is observable. However due to overlap with neighboringbands a multivariate data analysis will improve the accuracy of themethod significantly.

The area of the 1026 cm⁻¹ band is therefore extracted using Gaussiandeconvolution. As an alternative, variations of multivariate dataanalysis could also have been used. The results showing the integratedabsorbance at 1026 cm⁻¹ as a function of glycoside bonds per glucoseunit are plotted at FIG. 14 for the four spectra shown at FIG. 12. FIG.14 clearly demonstrates how accurate the method is to discriminate andidentify the glycoside bonds, even at low concentrations.

Example 2

A slurry of 3.5% w/w powdered starch in water is prepared and heated to90° C. for 5 minutes resulting in a homogenous gel like solution. Thesolution is then injected into a 5 mL vessel that is attached directlyon top of the ATR-plate, the vessel is designed in such a way that theATR crystal and the solution have intimate contact. Then the solutionwas stirred by an external stirring device at room temperature, whilespectra were continuously recorded. To simulate a mashing process, asolution of a-amylase was added to the solution. Hereafter the totalabsorption from sugar compounds increased, due to better solubility ofthe shorter-chain starch molecules, but the intensity stabilized withinfew minutes. The spectra in the 1200-900 cm⁻¹ region started to changesignificantly, and the bands intensity of the 1030-1020 bands decreasedsignificantly over time, indicating depolymerization of the starch, seespectra at FIG. 15, where the dark lines represent early time and thegrey lines represents the spectra after longer and longer time as thelines becomes more and more grey. The bottom dotted line in FIG. 15shows the spectrum of pure water for comparison.

After around 30 minutes, there were no further changes in the spectraindicating that the enzymatic process had almost stopped. Comparing thespectrum (light grey) with that of maltose and maltotriose in FIG. 12,it is clear that the solution now consist of a mixture of maltose andmaltotriose.

Example 3

0.1 g of protein extracted from barley is mixed with one gram of waterand heated gently at 60° C. for 15 min. The resulting slurry is stirredin a small vessel on top of an ATR-FTIR unit. This allows intimatecontact between the slurry and the diamond of the ATR unit, while IRspectra are recorded continuously. The spectra shows a characteristicabsorption (slightly overlapping) from the amide groups of 1690 cm⁻¹ and1550 cm⁻¹, respectively, the C═O stretching band and the N—H bendingband. After some time a 0.1 mL solution containing protease enzyme isinjected into the vessel. After the enzyme injection, the band at 1690cm⁻¹ shifts upwards to around 1705 cm⁻¹. While the band at 1690 cm⁻¹ ischaracteristic for the carbonyl stretching at the peptide bond in theprotein group, the 1705 cm⁻¹ band is characteristic for free amino aciddimers or a corresponding terminating amino acid group in a shortprotein fragment. The individual area of the 1690 and the 1705 cm⁻¹bands can clearly be resolved using deconvolution of the spectra and isused to calculate the dynamic a ratio of (dissolved protein)/(free aminoacids) throughout the protease treatment.

Example 4

60 kg of coarsely ground malted barley and 200 L water is loaded into a250 L stainless steel container supplied with a boiler unit for heatingand a mechanical stirring device. At the bottom of the tank, anelectronic controlled valve is connected for sample extraction. Beforethe valve is a coarse filter preventing the larger particles to passthrough the valve. During operation, the valve is opened so aperistaltic pump may extract samples from the tank though the valve.

The sample liquid is pumped into a chamber attached on top of a goldengate diamond ATR unit (see drawing at FIG. 4-5). IR spectra areconstantly recorded with 30 second intervals. Initially the barley isheated to 37° C. and maintained at this temperature for 20 minutes.Afterwards the mash is taken to 60° C. for 20 minutes, and then to 65°C. for 40 minutes.

At first, the total signal from sugars increases due to dissolution fromthe malt into the mash, but at elevated temperatures the areas of theintensity of the bands in the 1030-1020 cm⁻¹ region start to decreasesignificantly. The spectra are deconvoluted simultaneously with the realtime recording of the spectra and on the basis of the areas ofcharacteristic bands in the deconvoluted spectra and pre-obtainedcalibration curves, a ratio between fermentable sugars and maltodextrinsis obtained. When the mash contains the preferred ratio betweenfermentable sugars, the temperature is quickly raised to 75° C.denaturing the amylases and thereby stopping the mashing at the optimalcomposition.

Example 5

A reactor is loaded with 1000 kg of peels from potatoes and 2000 L ofwater. Then the resulting mixture is heated to 100° C. for 10 minutesand cooled to 50° C. At this temperature, valves are opened in thebottom and the top of the tank and the mixture is allowed to circulatethrough a side channel 108 in the reactor creating a loop similar tothat shown in FIG. 2.

Among other probes, a fiber based ATR-FTIR diamond probe is located inthis side channel. The probe is connected to an adjacent FTIR unit,which at this time starts to record spectra of the liquid passingthrough the side channel. The temperature is maintained at 50° C. andcommercial grade amylose powder is added to the reactor/mashing tank100.

Initially the spectra shows characteristic features of starch and longchain maltodextrins. But over the course of an hour, the intensity ofthe 1020-1030 cm⁻¹ region increases while a general signal increase overthe remaining part of the 1200-900 cm⁻¹ region is observed.

The spectroscopic change indicates that the potato starch is hydrolyzedwhile the sugar compounds in solution is increased due to the highersolubility of short chain starch derived compounds versus long chainstarch compounds. Simultaneously with the real-time recording of thespectra, each spectrum is deconvoluted. The intensity of all the bandsin the 1160-900 cm⁻¹ region is used to calculate to total concentrationof starch-derived compound in solution. The band at 1030-1020 cm⁻¹ isused to calculate the average degree of depolymerization. As soon as theoptimal composition is reached, the content of the reactor is pumpedfurther onto a fermentation unit.

Example 6

On a plant producing second-generation bio-ethanol fuel additives, thefeedstock containing pulverized straw and corn stover is initiallypretreated at elevated temperatures and pressures with a dilute sulfuricacid solution. During this treatment, the slurry is cooled, and thecrystalline cellulose fibers are all converted into amorphous cellulose.The amorphous lignocellulose is extracted, washed and water withbuffering agent is added. Then powdered commercial grade cellulase isadded and the temperature adjusted and maintained at around 50° C.

The process is monitored by analyzing the liquid part of the slurry thatis sent to a customized ATR-FTIR instrument 200, 300 adjacent to themashing unit 100. The sample is extracted continuously by filtering ofthe largest particles to prevent plugging. The filtered slurry istransferred to a special designed chamber on the ATR-unit of the FTIRinstrument.

For the first period of operation of the mashing, the signal increasessignificantly in 1200-900 cm⁻¹ region indicating the dissolution ofcellulose oligomers from the amorphous lignocellulose particles in theslurry. Later, the signal in the region starts to stabilize while amarked decrease in the 1020-1030 cm⁻¹ region is observed due to thehydrolysis of the oligomers into glucose. When the change/per minute inthe 1020-1030 cm⁻¹ region is below a certain threshold limit value, afeedback trigger message is automatically sent from the ATR-FTIRanalyzer to a process control system. At this threshold enzyme activityis decreased, and the slurry mostly consists of lignin particles andglucose and a minor part of cellobi- and tri-ose and higher chain lengtholigomers. The trigger message sent to the process control systemactivates a pump with filtration unit, and the filtrate is pumped into afermentation unit.

Example 7

A microscale mash is performed by mixing 225 g of ground, malted barleywith 1 L water at 54° C. in a conical flask during magnetic stirring. pHis adjusted by adding 0.25 g citric acid. The temperature is maintainedat 54° C. for 15 minutes. Then the temperature is raised to 65° C. for 1hr, and finally to 75° C. for 25 minutes. During all three temperaturesteps, aliquots of a few mL is extracted using a pipette during themashing, and transferred to a test-tube on a boiling water bath for 5min to ensure denaturation of enzymes. Then the precipitate is removedby centrifugation, and an ATR-IR spectra is obtained of the remainingclear liquid phase, and the refractive index in Brix % is determinedusing a refractometer. The Brix scale is often used in the food andbrewing industry, and is a convenient way to describe refractive indicesso they relate directly to the total amount of dissolved carbohydratesin solution.

All spectra are ATR corrected assuming a refractive index of 1.36 usingThermofisher OMNIC software and a spectrum of pure water was subtractedfrom each spectrum of the mash. Then the integrated absorbance from 950cm⁻¹ to 1260 cm-1 is determined and plotted as a function of the Brix %value of the same sample. The plot is shown in FIG. 16.

The relationship between the two shows that the summed integrated IRabsorption in the difference shows a very accurate relationship with thetotal amount of dissolved carbohydrates during mashing. Hence, the totalamount of dissolved carbohydrates during mashing can be monitoredaccurately in real time using ATR-IR spectroscopy.

Example 8

In the literature, it has been described that the particulates of themashing would cause problems in the application of ATR-IR spectroscopy,as the starch granules and husk particles would affect the spectra ofthe solution phase. This seem to be a common perception among skilledscientist in the field.

Although it is observed to be partly true for very early times of theexperiment where some of the ultra-small starch granules indeed seems tocontribute slightly to the spectra, this has very little technicalinfluence on this invention. As the solid-state particles are only foundto influence the monitoring at very early times of the experiment; i.e.the granules has no effect during the interesting stages of mashing(after the first few minutes).

In the laboratory a mashing according to the procedure in the previousexample 7 is performed. Around 20 minutes into the mashing a sample ofapproximately 2 mL is transferred to a test tube in an ice bath toquench the enzymatic hydrolysis reaction. The sample is at this pointvery slurry and appear milky and opaque due to the fine starch granulesand fine husk particles. Then some of the yellowish slurry istransferred with a pipette to a FTIR-spectrometer with a diamond ATRdevice, and an IR spectrum is recorded while the sample is constantlykept in motion using a pipette, i.e. stirred with a pipette. Spectra aretaken of the static sample without keeping it in motion. Finally thecrystal is thoroughly cleaned with a moist tissue and some more of thesame sample is transferred to the ATR crystal.

The last time the sample is transferred to the ATR crystal using asyringe equipped with a 0.22 μm syringe filter. The spectrum of thisclear liquid is identical to the previous spectrum of the slurry sample,demonstrating the particulates in the mash does not influence therecorded spectra. The three spectra are compared at FIG. 17 showing thesame sample aliquot during mashing, with and without filtration. In thecase of the unfiltered sample, the effect of stirring the sample whilerecording is investigated.

The illustration in FIG. 18 shows the mechanism behind the phenomenonobserved in FIG. 17. As long as the particles in the slurry aresignificantly larger than the penetration, depth of the evanescent wave,their contribution is negligible to the recorded spectrum. I.e. therecorded spectrum is in practice independent of the particulates andonly the solvated part of the slurry is ever recorded. The focusedevanescent wave will only penetrate a very thin film of the liquid. Thepenetration depth of the evanescent wave is magnitudes smaller than thetypical starch granules and husk particles in the slurry, in practiceexcluding the granules from the spectra, and obtains a spectrum of thepure liquid.

Example 9

A chemometric model based on IR spectra in the fingerprint region modelmust rely on all possible details to discriminate between very similarcompounds with reasonable accuracy, especially with the respect to thepresent invention where small changes in structure of bio-molecules haveto be quantified using ATR-FTIR. First of all it is naturally worth toconsider the spectroscopic changes due to primary structure changes. Inan enzymatic hydrolysis of a biopolymer, this would be the changesdirectly related to the breakage of polymeric bonds. Here the directspectroscopic change would be related to the disappearance of theprimary group frequency bands of either peptidic bond in proteins orglycosidic bonds in carbohydrate respectively.

However new spectral features due to secondary structure may be crucialto consider. The secondary structure changes should here be understoodas changes that occurs or becomes possible as indirect consequence ofthe primary structure change e.g. breakage of a glycosidic or peptidebond during the mashing.

This example shows how the present invention rely on a moresophisticated method, than is normally used in spectroscopy basedchemometrics that often rely solely on blind statistical analysis. Inthis method it is important to: 1) identify, 2) understand and then 3)rationally quantify and exploit secondary structure changes suddenlybecoming possible or indirectly induced by the primary enzymaticprocess. The breakage of e.g. a peptide bond would not only result inthe loss of the characteristic vibrational modes of the peptide bond butalso result in the creation of both an amine group as well as acarboxylic acid.

In such cases the most important discriminators are most likely thesesecondary changes. In this example especially, the appearance ofsignificantly different modes due to the dramatic changes in thesymmetry, especially if the product is ionic, is at least as useful asthe primary structure change. This is illustrated in FIG. 19 showinghydrolysis of the peptide bond allowing several new species to develop,with very different symmetric and spectroscopic properties. However,unilateral statistical use of the new illustrated spectroscopicsignatures would require a very large calibration set. But what wouldfurther make this approach very limited in a technical sense, is that itwould only be valid in a very narrow pH range. The approach presentedhereunder applies a qualitative interpretation along with aquantification and result in a model that is much more rich andversatile in its predictions.

As shown in FIG. 20, the hydrolysis of amylo-starch facilitates asecondary structure change, as the reducing end will not remain in theα-pyranose form but engage in an equilibrium with the beta form. Inaqueous solution, D-glucose exists as an equilibrium between the α- andβ- of glucopyranose forms, in a ratio approximately 34%:66%respectively. This equilibrium is very dynamic and the two forms areinterchangeable through mutarotation.

In the original amylose fragment form all anomeric C—O groups are in theα-configuration (see the dotted circle at (a)). During the hydrolysis a“reducing end” is made. At the reducing end the carbohydrate cantransform into the beta anomer, which is slightly more stable than theα-anomer. The same is true for the reducing end of di-, tri-, oligio-and polysaccharides which can also undergo mutarotation.

There is a significant difference between the spectroscopic signature ofthe α- and β-glycopyranose as is demonstrated in FIG. 21a and FIG. 21 b.

FIG. 21a shows the conversion of the pure anomeric α-glycosyranose formthe racemic mixture at room temperature. The dotted line shows the pureα-form. Lighter tones in the spectra corresponds to earlier timestampsin the experiment whereas darker corresponds to later timestamps.

FIG. 21b shows the comparison of the pure dissolved α-D-glucopyranosewith racemic mixture of α and β-forms D-glucopyranose at equilibrium.

The spectra are obtained by rapidly dissolving 12.5 w/v pure crystallineα-D-glycopyranose (Sigma-Aldrich) in water and continuously recordingspectra of the solution using a germanium ATR cell at room temperaturefor 90 minutes (first spectra recorded in less than 1 minute from thestart of mixing). Then the sample is covered to avoid evaporation.During the experiment, the change of the pure α-D-glycopyranose form ofglucose to the mixture of both α and β forms can be observed in situ.After 90 minutes no changes in spectral features is observed, hence theα and β forms has reached equilibrium.

The spectra is identical to samples stored for more than 24 hr. Twosignificant spectroscopic handles are that the intensity of the 1080cm⁻¹ band is much strong in the β form, whereas the 1055 cm⁻¹ and 1150cm⁻¹ bands are much more intense in the α-form. As the IR active modesin the fingerprint area in the most cases has a very complex and mixedorigin each comprising several OH and CH bending mode combined withvarious C—O stretching mode the re-orientation of a single C—O group canget several spectroscopic modes to change.

This is, on top of the measurement of the group vibrations of theglycoside bond itself, also a good measure of the hydrolysis, as itallows the concentration of “reducing ends” to be quantified.

However, real-time use of the spectral features of the beta form as ameasure of hydrolysis, would require that the rate of the mutarotationis significantly faster than the rate of starch hydrolysis. If thekinetics in the anomeric equilibrium was slower than the rate ofhydrolysis, this would make real time chemometric modelling of theprocess very challenging.

The kinetics at the equilibrium is, as shown in FIG. 20, relatively slowat room temperature. It is indeed not possible for a person skilled inthe art in general to make a final judgement on how the kinetics of theequilibrium vs. the hydrolysis is at elevated and more realistic processtemperatures.

Therefore, the in situ mutarotation is performed at 60° C. which is moretypical for most enzymatic hydrolysis processes, e.g. mashing of maltedcereals, as this temperature is close to the gelation temperature ofstarch. The heating is introduced by a homemade electrical heatingdevice placed on top of the Germanium ATR device that can maintainisothermal conditions. The temperature is further monitored, by athermocouple very close to the germanium crystal. Then α-D-glucopyranoseis mixed with water at room temperature and then quickly transferred tothe 60° C. hot germanium crystal covered by the heating block while IRspectra are recorded continuously. The IR spectra are shown in FIG. 22showing in situ conversion of α-Glycopyranose, i.e. the racemic mixtureof the α and β forms at 60° C. The dotted line is the spectrum of thepure α form at t=0. Lighter tones in the spectra correspond to earliertimestamps in the experiment whereas darker corresponds to latertimestamps.

The experiment clearly demonstrates that the kinetics are very fast attypical mashing conditions, and bands associated with the increasedamount of β-glycopyranose form in shorter oligosaccharide chains is agood alternative measure of the overall hydrolysis of starch.

The experiment is performed both with and without 0.25 w/v % citric acidadded to the water prior to dissolution of the α-glycopyranose, and themutarotation rate is observed to be independent of the presence of thesmall amount of citric acid. The example clearly demonstrated theimportance of this unexpected and indirect spectroscopic handle duringhydrolysis. Analogous this would apply for related systems. An exampleis in the case of cellulose (β-(poly-(1,4)-glycopyranose) hydrolysis,where the hydrolysis form would result in a higher amount of theα-glycopyranose due the mechanism described above.

Example 10

Various types of maltodextrins are available commercially, allcharacterized by their reducing power in dextrose equivalent (DE). TheDE number is measured by the sugars capability of reducing Cu(II) toCu(I), and has been the industry-standard way to measure DE values forcarbohydrates. Where pure glucose is defined as DE=100 while maltose andmaltotriose e.g. has DE values of 50 and 33 respectively, a maltodextrinof an average degree of polymerization (ADP) 8 would have a DE numberof:

DE=⅛=12.5  [2]

For starch-derived oligosaccharides, the average degree ofpolymerization is the reciprocal DE value.

DE=1/(ADP)  [3]

The reduction is performed in large surplus of Cu(II). As Cu(II) is muchmore colored than the pale slightly bluish-green Cu(I), the traditionalmethod of determining the Cu(I) concentration after reduction has beenthe Lane-Eynon titration, or by gravimetric methods, which are bothrather laborious methods. Further, these methods are inherentlyimpossible to do in real time.

In here is adapted a newer and well established method, which allows theCu(I) concentration to be determined colorimetric as a strong purplecomplex when 2,2-bquinoline-carboxylate anions are added. Further themoisture content of all maltodextrins are determined graviometrically byheating all sugars at 105° C. for 1 to 2 hours, and the DE and ADPvalues are calculated with respect to the dry fraction of themaltodextrins.

The use of this well-established orthogonal technique is found to be agood way to validate accuracy the method applied by the currentinvention. Reagents are prepared according to (Zhang, Y.-H. P.; Lynd, L.R.

Biomacromolecules 2005, 6, 1510-1515.). The reaction time at 75° C. isincreased from 30 minutes to exactly 60 minutes. 5 different dilutionsis used for each carbohydrate and the concentration of the2,2-bquinoline-carboxylate-Cu(I) complex is measured via the absorbanceat 560 nm. The DE value is determined from slope by linear regression,all with R² values greater than 0.999, by comparison with a glucosestandard. The DE values are given in Table 1.

TABLE 1 ADP values for commercial starch hydrolysis products, obtainedby using coloriometric determination of the sugars reducing power ofCu(II) with respect to a glucose standard. Carbohydrate “MaltodextrinMaltose Maltotriose DE = monohydrate hydrate 16.5-19.5” (sigma-aldrich)(sigma-aldric) (sigma-aldrich) Glucidex12 ADP (from Cu(II) 2.01 3.025.54 10.5 reduction) Corresponding 0.502 0.669 0.819 0.905 RDP

As ATR-IR spectroscopy is a way to do transmission IR spectroscopy, andas the penetration depth at a certain wave length will always beconstant for samples with similar refractive indices, Lambert Beers lawas presented in equation 1 is directly applicable.

To further make the analysis of ADP values generic for allconcentrations substrate concentrations during mashing a term denotedthe Relative Degree of Polymerisation (RDP) is introduced. In thegeneral case of a synthetic or biopolymer the RDP would be:

RDP=n _(polymeric_bonds) /n _(total_monomeric_units)  [4]

In the special cases hydrolysis products from either starch or celluloseRDP is defined:

RDP=n _(glucosidebonds) /n _(total_glucose_units)=(n_(total_glucose_units)−1)/n _(total_glucose_units)  [5]

E.g. the monomer will have a RDP value equals zero, the dimer will havea value of 0.5, while the heptamer of 6/7 and so forth. With thisintroduced concept of the RDP, it became possible to create universalcalibrations to determine the average degree of polymerizationregardless of the absolute carbohydrate concentration.

Solutions of 15 wt. % carbohydrates are prepared, of each of thecarbohydrates presented in Table 1. Spectra of all samples shown in FIG.23 were recorded using a 45° Diamond ATR device. The model used todeconvolute the spectra are indicated in the spectra.

All spectra in FIG. 23 are analysed the following way: The spectra areATR corrected to approximate the corresponding true transmissionspectra, then a background of water is subtracted from each spectra.Finally each of the ATR-corrected difference spectra are deconvolutedusing a model comprising pseudo-voigt functions. During deconvolution,position and bandwidth of the distribution curves are constrained andonly the amplitude is optimized, to ensure that the bands do not migraterandomly during deconvolution. To accommodate some flexibility due toslight shifting of the bands, in some cases some bands are split intotwo bands with positions very close to each other. E.g. the bands ataround 1080 and 1152 cm⁻¹ is respectively represented by the sum ofbands at 1078+1081 cm⁻¹ and 1148+1154 cm⁻¹. Each of the obtained bandareas are normalized by dividing by the sum of all bands from thedeconvolution. Dividing by the total area is equivalent to normalizingwith the total dissolved carbohydrate concentration. This is clearly avery good approximation as demonstrated in example 8 clearly showingthat the total dissolved carbohydrate concentration is proportional withthe integrated area of all of these bands.

By plotting several of the deconvoluted band areas against the RDPvalues obtained from the DE values from the Vis spectroscopicCu(II)-Cu(I)-2,2-bquinoline-carboxylate, as shown in FIG. 24, it isshown that by applying the new method, the same values can be obtainedfrom IR in real-time, without having to ad reagents. This is a hugeadvantages both in terms of simplicity of the method and the ease withwhich it can be conducted but also in terms of costs.

FIG. 24 shows the integrated band areas from the deconvolutions of thefour spectra shown in FIG. 23, showing very good statisticalcorrelations with the RDP values of the carbohydrates.

Example 8 shows that the IR spectra can be used to establish good valuesfor the total amount of dissolved carbohydrate during mashing. On top ofthis the present example 10 shows that the present invention can be usedto accurately determine the average degree of polymerization of thetotal dissolved sugars, which is a value that cannot be rapidly obtainedby any other means.

Example 11

As discussed above in example 10, maltodextrins are often characterizedthrough their DE values. However, the DE number alone does not describemaltodextrin completely, as the ratio of α-(1-4) and α-(1-6) glycosidebonds can vary independently of the DE number. The maltodextrin used ine.g. calibrations studies must have a realistic distribution of bothα-(1-4) and α-(1-6) glycoside bond.

Starch is primarily composed of glycose linked with α-(1-4) glycosidebonds, which makes the majority of the glucoside bonds the α-(1-4) type.However especially the amylopectin part of starch is branched due to asmaller amount of α-(1-6) glycoside bonds. Due to this branching roughlyaround 5% of the glycoside bond in typical starch is α-(1-6) while therest is α-(1-4). As the most of the amylases can only hydrolyse theα-(1-4) glycoside, the majority of the native α-(1-6)-glycoside bonds inthe starch stays intact during the hydrolysis process, as they are muchmore resistant to most amylase attacks. Therefore the amount of α-(1-6)glycoside bond residue in the final hydrolysis of most starches,contributes significantly to the glycoside bonds of the unhydrolyzedoligosaccharides, and cannot be ignored from a spectroscopic point ofview.

Thus a solution of iso-maltotriose(α-D-glycopyranose-(1,6)-α-D-glycopyranose-(1,6)-D-glucose,Sigma-Aldrich) is used as a reference for the pure spectral features ofthe α-1,6-glycoside linkage. Further, the commercial and food approvedα-(1,6)-glucose-oligosaccharide VitaFiber is purchased from differentdistributors. Although VitaFiber is mainly composed ofα-(1,6)-glucose-oligosaccharide with a smaller fraction of mixedα-(1,6)/α-(1,4) glucose-oligosaccharides. These two α-(1,6) glycosidiccompounds is then compared to a commercial maltodextrin (ADP=5,54,sigma) and pure maltotriose.

FIG. 25 shows spectra of 12.5 w/v % solution of iso-maltotriose,VitaFiber oligosaccharide, SigmaAldrich maltodextrin, Maltotriose andwater. The difference in the absorption at around 1000-1030 cm⁻¹ shows asurprising difference between the spectroscopic features of the α-(1,4)and α-(1,6) glycoside bonds.

It is quite apparent that the isolated α-(1,6) bonds are quitedifferent, and α-(1,4)-maltotriose and iso-maltotriose showedsignificant differences. Especially at around 1025 cm⁻¹ the absorptionof the acetal vibration modes are significantly stronger in the α-(1,6)isomers. Further, the 1155 cm⁻¹ mode of the antisymmetric glycosideC—O—C stretching mode is shifted slightly up to around 1160 cm⁻¹.

The VitaFiber and the short commercial maltodextrin are both found inbetween the two pure samples (iso-maltotriose and maltotriose), showingthat they both contained a significant amount of both types ofglycosidic bonds. Especially since the maltodextrin is manufacturedthrough partial hydrolysis of natural starch, it is a very realisticmodel for the maltodextrin development during real mashing processes.Therefore it is concluded the low ADP maltodextrin purchased fromSigma-Aldrich would serve as a realistic model with a realistic ratio ofα-(1,6)/α-(1,4) glycosidic bonds. Further this example shows that thepresent invention, under the right circumstances, is able todiscriminate and quantify different types of glycosides in mixtures.Especially taking the example 9 into account, the above results, incombination with a deconvolution and/or statistical analysis, can beused to build a multivariate model that can discriminate and quantifythe types of glycoside bond in addition to the ADP value in real timeduring a mashing process.

Example 12

During the last 50 years the exact location of the glycosidic vibrationin polymeric carbohydrates have been discussed greatly. Severalcontroversies have been presented throughout the years based onarguments and indices, without any solid proof from experiments alone,on where to locate the characteristic frequencies of the glycoside bond.Therefore, it seems apparently impossible make an in-depth analysis ofthe fingerprint region of IR spectra of carbohydrates, even for a personsimultaneously skilled in the art of spectroscopy and chemistry. This isespecially true for features that are related to the degree ofpolymerization.

Previous examples in the literature has failed to use statisticalanalysis such as partial least square (PLS) analysis, to discriminatesimilar di- and trisaccharides like maltose and maltotriose based onexperimental IR Data. When otherwise well-proven statistical techniquesfor previous studies, to discriminate very simple mixtures ofcarbohydrates, has failed, it has led to the assumption that it is notpossible to use IR to discriminate the sugar types and their degree ofpolymerization.

From the electronic structure, the quantum mechanical normal modes ofthe molecule can be calculated, as well as a good estimate of theoscillating dipole as a function of the normal mode. This means that avery realistic infrared spectrum can be generated from the electronicstructure. This allows for much more qualified interpretation of theexperimental spectra, as each band in the experimental spectra can beassigned with a quantum mechanically well described normal mode.

The normal modes of amylose are studied quantum mechanically usingdensity functional theory on glucose, maltose and maltotetraose. Firstthe energy of the electronic structure of each carbohydrate wasminimized by using the B3LYP functional and the 6-31G(d) basis set usinga commercially available software package. Then the IR spectra iscalculated, and the calculated spectra is corrected for anharmony with ascaling factor of 0.98 in accordance with common practice in literature.

The theoretical spectrum is in good agreement with the experimentalbands, and reveals that the glycosidic linkage is involved in severalcharacteristic vibrational modes. The medium intense band at 1155 cm⁻¹is assigned to the antisymmetric C—O—C vibration of the glycoside band.Although glucose and the non-reducing ends also have absorptions in thesame region, due to the C4-O stretching, these vibrations occur atslightly lower wavenumbers and at significant lover intensities. Strongbands at around 1035-1000 cm⁻¹ can be related to modes comprising strongvibrations of the O—C—O stretching modes around the acetal groups of theglycopyranose rings. The α-(1,4) glycoside bonds gave rise to a mediumstrong absorption while the similar mode in the case of α-(1,6)glycoside bonds has even stronger absorption, which again explains ofthe difference of the presented spectra for maltotriose andiso-maltotriose shown in FIG. 25.

Example 13

Three stem solutions were prepared: 15% w/v glucose (anhydrous, watercontent <1%), maltose (15% w/v with respect to mass of maltosemonohydrate, Sigma Aldrich) and maltodextrin (ADP=5.54, purchased fromSigma-Aldrich). Then mixtures of these, according to table 2 areproduced, by mixing with a high precision auto pipette. An FTIR spectrumof each of the 21 calibration standards is recorded using a horizontal10 reflection germanium 45° ATR cell and a Nicolet iS5 spectrometer. Allspectra are ATR corrected using a refractive index of 1.36, for allsamples. A deconvolution analysis of all 21 samples is performedanalogous to the method described in example 10, but with slightlyadjusted parameters. Further the water background used to generate thedifference spectra is an analytical approximation by a higher degreepolynomial. Further an algorithm is applied that corrected the spectrafrom drift, by analysing several absorptions at higher and lower wavelengths where the overall absorption from the carbohydrate features islower.

TABLE 2 entry Glucose maltose maltodextrin RDP 21 30% 70%  0% 0.350 2025% 70%  5% 0.391 19 20% 70% 10% 0.432 18 15% 70% 15% 0.473 17 10% 70%20% 0.514 16  5% 70% 25% 0.555 15  0% 70% 30% 0.596 14  0% 65% 35% 0.61313  0% 70% 30% 0.596 12  0% 75% 25% 0.580 11  0% 90% 10% 0.532 10  0%95%  5% 0.516 9  0% 98%  2% 0.506 8  0% 100%   0% 0.500 7 15% 50% 35%0.538 6 15% 55% 30% 0.521 5 15% 60% 25% 0.505 4 15% 75% 10% 0.457 3 15%80%  5% 0.441 2 15% 83%  2% 0.431 1 15% 85%  0% 0.425

In example 10, it is shown that IR can determine ADP values of pure di-and oligio saccharides. However, in most technical cases of mashing thedifferences in ADP values are far smaller.

In FIGS. 27 and 28, ten band or combinations of band areas are presentedas a function of RDP of the mixtures.

FIGS. 27-28 show correlations between deconvoluted band areas of spectrain FIG. 26 and the relative degree of polymerization at differentwavelengths. The different regression plots compare the whole datasetwhere samples containing either glucose or maltodextrin respectively areremoved.

The plots presented are chosen as they all show a fair to goodcorrelation with the RDP value, whereas the plots only showing very poorstatistical correlation are not shown. Especially for the plots showingthe correlation between the RDP and the 1078+1081 cm⁻¹, 1110 cm⁻¹ and1078+1081+1110+1130 cm⁻¹ summed band areas respectively all show verygood correlation; all with R² values close to or above 0.98. These bandswere shown, in example 9, to correlate with the C—O stretching+OH/CHbending specific to either the α or β D-glycopyrano-backbone. Thesebands seem to be the overall preferred of a universal descriptor in thiscase to determine the RDP during mashing at a certain time, regardlessof the specific carbohydrate composition. The band at 1036 cm⁻¹ does inthis example also exhibit very good correlation with RDP, but it will beshown later that this is only true in specific cases. Later it will beshown how the 1036 cm⁻¹ band can be exploited to supply additionalcompound specific information on the sample composition.

Different parts of the dataset were used for statistical analysis tomake a comparison with specific compound correlations with the RPDvalues. The correlation between RDP and the above-mentioned bands(1078+1081 cm⁻¹, 1110 cm⁻¹, 1078+1081+1110+1130 cm⁻¹ and 1036 cm⁻¹)showed no significant changes upon variation of samples that wereincluded in the dataset. This further shows that they, in the case ofmixtures of glucose, maltose and maltodextrin, are good universaldescriptors for the RDP regardless of the specific sample composition.

However, it largely increases the usefulness of the present inventionthat the above universal trend does not apply to all integrated bandsand RDP correlations. For the cases of correlation of RDP and band areasfor the 1003 cm⁻¹, 1022 cm⁻¹ bands as well as the sum of the two, theoverall correlation at first seemed poor and noisy compared to the abovedescribed bands (1078+1081 cm-1, 1110 cm-1, 1078+1081+1110+1130 cm-1 and1036 cm-1).

However, when only parts of the dataset were taken into account thepicture was very different. Especially in the cases where all samplescontaining glucose was removed before the regression, the correlationbecame significantly better (R² increased from 0.85 to around 0.975) andthe equation describing the correlation changed with a markedlydifferent slope. Similar, the correlation improved when removing allsamples containing maltodextrin. This clearly shows that the apparentscattering of data for these bands, are not noise. These bands are, ontop of correlating somewhat to the overall RDP, very dependent on thespecific carbohydrate composition of the sample. From this representedanalysis it is obvious that these bands play a special role inmultivariate calibration during mashing of starch containing feedstock,where it seems especially powerful in the estimation of the glucoseconcentration, on top of the bare average degree of polymerization.

An example of exploiting the compound specific information embedded inthe 1003 cm⁻¹ and 1022 cm⁻¹ bands in a multivariate calibration, couldbe to compare the divergence of the RDP values obtained with 1003-1022cm⁻¹ bands and the above used 1078-1130 cm⁻¹ bands: The 1078-1130 cm⁻¹bands seems to be much more universal in their predictability of the RDPvalues in starch mashing.

While both glucose and maltose is fermentable sugars, glucose is presentin the final mash in large concentrations (10-30% of totalcarbohydrates), and is typically the 2^(nd) most abundant carbohydratein the mash, after maltose. A low glucose to maltose ratio will resultin relatively high RDP value, without the presence of a lot ofmaltodextrin and vice versa. The above-demonstrated principles formultivariate modelling that would make it possible to estimate theglucose concentration in the mixture, is therefore of great technicalimportance in mashing processes, especially in the brewing industry. Anaccurate value of the RDP as well as a good estimate of the glucoseconcentration will facilitate real-time calculation of real degree offermentability during mashing, which is of great technical value for thebrewer.

At last, all samples are diluted down to 11% w/v and 7% w/vrespectively, and all samples are recorded in their diluted states. Thedata are treated the same way as described above, and it is found thatthe statistics is close to identical to the above presented correlation,which showed that the RDP calibration method indeed providesconcentration independent calibrations.

Example 14

The previous example 13 shows how the invention can give estimates forthe RDP value as well as the glucose concentration, which can be used toestimate the degree of fermentability. A related challenge arise fromthe occurrence maltotriose, which, from a spectroscopic point of view,is similar to maltose. In most mashing processes of starch basedfeedstock, a significant amount of maltotriose is produces as aby-product (up to 5-20% of total carbohydrate). Especially in the caseof mashing processes for the brewing industry, it would be of greattechnical importance to estimate concentrations of maltotriose. This isespecially relevant since it is non-fermentable by most types of yeast,and hence contributes with taste in the final product, and is often inthe brewing industry just referred to as a general maltodextrin.

Hence another calibration set is made according to the proceduredescribed in example 13, this time including maltotriose in variousconcentrations, making the four mixed carbohydrates a very realisticmodel system for actual starch mashing. The sample composition can beseen in table 3 and the recorded spectra are shown in FIG. 29.

TABLE 3 ENTRY glucose maltose maltotriose maltodextrin 1  0% 95%  0%  5%2  0% 85% 10%  5% 3  5% 85%  5%  5% 4 10% 85%  0%  5% 5  5% 75% 15%  5%6 10% 75% 10%  5% 7 15% 75%  5%  5% 8 20% 75%  0%  5% 9  5% 65% 25%  5%10 10% 65% 20%  5% 11 15% 65% 15%  5% 12 20% 65% 10%  5% 13  5% 55% 35% 5% 14 10% 55% 30%  5% 15 15% 55% 25%  5% 16 20% 55% 20%  5% 17  0% 75%10% 15% 18 10% 75%  0% 15% 19  0% 65% 20% 15% 20 15% 65%  5% 15% 21 20%65%  0% 15% 22  0% 55% 30% 15% 23  5% 55% 25% 15% 24 10% 55% 20% 15% 2515% 55% 15% 15% 26 10% 55% 20% 15% 27  5% 55% 25% 15% 28  0% 75%  0% 25%29  5% 65%  5% 25% 30 10% 65%  0% 25% 31  0% 65% 10% 25% 32 20% 55%  0%25% 33 15% 55%  5% 25% 34 10% 55% 10% 25% 35  5% 55% 15% 25% 36  0% 55%20% 25% 37  0% 45% 30% 25% 38 10% 45% 20% 25% 39 20% 45% 10% 25% 40 30%45%  0% 25% 41  0% 60%  0% 40% 42  5% 50%  5% 40% 43 10% 50%  0% 40% 44 0% 50% 10% 40% 45 10% 40% 10% 40% 46  0% 30% 30% 40% 47 10% 30% 20% 40%48  5% 30% 25% 40% 49  0%  0%  0% 100% 

The same statistical analysis is performed on all deconvoluted andnormalized band areas as in example 13. The result of the regressionanalysis can be seen in FIGS. 30-33. FIGS. 30-31 are similar to thepresented data in FIGS. 27-28 and shows the different band areascorrelation with RDP, with the exclusion of glucose, maltodextrin andmaltotriose containing samples respectively. The deconvolution modelparameters has been tuned slightly with respect to the ones presentedpreviously. This is also the case for the drift correcting algorithm aswell as the water subtraction procedure, which now used an experimentalbackground instead of the analytical approximation.

The statistical trends presented in the previous example are reproducedvery well with the new dataset. The trend can be seen at FIGS. 30-31,regarding correlation between the 1078+1081 cm⁻¹, 1110 cm⁻¹ and1078+1081+1110+1130 cm⁻¹ bands, this time with very good statisticevidence due to the larger size of the data set. All these correlationsare very good, and the resulting equations are almost identical whencomparing the whole dataset with cases where parts of the dataset whereexcluded. The 1148+1153 cm⁻¹ band also behaves as in the previousexample, and still seems as a good descriptor for RDP although it isslightly noisier. It also seems like the dataset reveals that this bandmay include some more compound specific details, with some variation inthe resulting equation, however the number of analysed samples is toosmall for the analysis to be conclusive regarding the 1148+1153 cm⁻¹band.

The analysis of the 1003 cm⁻¹, 1022 cm⁻¹ and 1003+1022 cm⁻¹ band areacorrelation with RDP shows a very accurate reproduction of the resultspresented in Example 13, and confirms that these bands are very usefulin the discrimination of glucose in the samples. However, it isinteresting to notice that the exclusion of maltotriose, result inalmost identical equations as the whole data set for the 1003 cm⁻¹, 1022cm⁻¹ and 1003+1022 cm⁻¹ band areas correlation with RDP.

The 1036 cm⁻¹ band area correlation with RDP is very different from themaltotriose free study presented in the previous example. In theprevious example the 1036 cm⁻¹ band area is correlated very well withRDP, for the whole dataset, as well as in the two cases of exclusion ofglucose and maltodextrin. Here the correlation for the whole datasetseems overall poor and noisy at first, with R²=0.82. However, byexclusion of maltotriose rich samples (set point 5%) the trend changessignificantly; the apparent noise is reduced, and the rest of the datanow correlate well with RDP with a R²=0.943.

To investigate the maltotriose influence on the dataset in details, themaltotriose rich samples are investigated in further detail by alsoinvestigating the band area correlation with RDP, by excludingmaltotriose poor samples. These plots are shown in FIGS. 32-33.

The band areas 992 cm⁻¹, 1003 cm⁻¹, 1022 cm⁻¹, 1003+1022 cm⁻¹, 1078+1081cm⁻¹, 1110 cm⁻¹, 1130 cm⁻¹, (1078+1081+1110+1130 cm⁻¹), 1148+1153 cm⁻¹correlation was constant, regardless if the dataset comprised allsamples, maltotriose poor samples, or maltotriose rich samples. Howeverthe 1036 cm⁻¹ band area correlation with RDP shows to be very sensitiveto the concentration of maltotriose. Even including all samplescontaining down to 10 and 15% maltotriose, of the total amount ofcarbohydrates, showed to improve the statistics of the correlation aswell as changing the slope of the resulting equation.

FIGS. 32-33 shows correlations between deconvoluted band areas ofspectra in table 3 and the relative degree of polymerization. Thedifferent regression plots compare the whole datasets bands areas vs.RDP, bandareas where only including maltotriose rich samples.

Example 15

In this example, 150 samples are used by mixing aqueous 12% v/wsolutions glucose, maltose, maltotriose and maltodextrin. The purpose ofthe experiments is to enhance the accuracy of multivariate calibrationthat allows better estimation of each specific carbohydrate compound.The samples are mixed in a way that allowed a range of different RDPvalues. The calibration set comprised 5 subsets of each 30 samples; thefirst subset containing all of the four carbohydrate components(glucose, maltose, maltotriose and maltodextrin). In the subsequent foursubsets, only three compounds in each subset is included, excluding thelast carbohydrate component. i.e. the 5 subset included 30 samples witha range of different combinations of the individual compounds leading toa range of RDP values:

-   -   1) Glucose, maltose, maltotriose and maltodextrin    -   2) maltose, maltotriose and maltodextrin    -   3) Glucose, maltotriose and maltodextrin    -   4) Glucose, maltose and maltodextrin    -   5) Glucose, maltose and maltotriose

An ATR-IR spectra is obtained for each sample, and their ATR correcteddifference spectra is treated and deconvoluted accordingly to example14. However, in contrast to example 13 and 14 Partial Least SquaresRegression (PLS) is performed instead of the manual comparative linearregression analysis. With the improved statistics of PLS it is possibleto establish a multivariate model that can be applied to on-line/in-linemonitoring during mashing of various starches. The multivariate model isapplied on the mashing liquid in real time to supply accurate estimatesof the total dissolved carbohydrate and RDP as well as estimates of thefour individual carbohydrates from above (glucose, maltose, maltotrioseand maltodextrin). These values are further used to calculate anaccurate real time estimate of the degree of fermentability of the mash.

Example 16

Another calibration comprising 280 sample solutions is producedaccordingly to the procedure in examples 13-15. However, maltotetraoseis added as a fifth component, as well as a long chain maltodextrin (LowDE value) as the sixth component. Maltotetraose is obtained from acommercial high-maltotetraose corn syrup which is very high in thecontent of maltotetraose, with minor fractions of maltotriose andmaltopentatose and almost no glucose and maltose. Using the HPLCcertificate supplied by the manufacturer the exact amount ofmaltotetraose and maltotriose in each sample can be calculated. Thecalibration set is split up into seven subsets, each comprising 40individual samples with different mixtures of the six components:

-   -   1) Glucose, maltose, maltotriose, high maltotriose syrup,        maltodextrin (DE≈18) and maltodextrin (DE≈5)    -   2) maltose, maltotriose, high maltotriose syrup, maltodextrin        (DE≈18) and maltodextrin (DE≈5)    -   3) Glucose, maltotriose, high maltotriose syrup, maltodextrin        (DE≈18) and maltodextrin (DE≈5)    -   4) Glucose, maltose, high maltotriose syrup, maltodextrin        (DE≈18) and maltodextrin (DE≈5)    -   5) Glucose, maltose, maltotriose, maltodextrin (DE≈18) and        maltodextrin (DE≈5)    -   6) Glucose, maltose, maltotriose, high maltotriose syrup, and        maltodextrin (DE≈5)    -   7) Glucose, maltose, maltotriose, high maltotriose syrup,        maltodextrin (DE≈18)

Again, the spectra data are deconvoluted and PLS analysis was performed,to build a multivariate model. The multivariate model can again beapplied in real time to supply accurate estimates of the total dissolvedcarbohydrate and RDP as well as estimates of the four individualcarbohydrates from above (glucose, maltose, maltotriose andmaltodextrin). These values are further used to calculate an accuratereal time estimate of the degree of fermentability of the mash. Furtherthis multivariate model can make some justified estimates of thefractions of the maltodextrins. The length of the maltodextrin is oftenimportant in the brewing industry as short maltodextrins likemaltotriose and maltotetraose are significantly sweeter than the oneswith higher degree of polymerization.

Example 17

10.0% w/v solutions of fructose, glucose, sucrose and 1:1 mixture ofglucose-fructose is prepared and allowed to equilibrate. ATR-IR spectraof the samples, using golden gate ATR device, are recorded and shown inFIG. 34. The spectra of the 3 sugars shows very different spectroscopicfeatures. It is interesting that the 1:1 mixture of glucose-fructose ismarkedly different from sucrose. This example, in combination with theexamples mentioned above, clearly demonstrates how the present inventionclearly can be applied to monitor sucrose hydrolysis in the productionof invert syrups, or in the enzymatic production of fructose syrups fromglucose syrups.

Example 18

A version of the mashing analyzer similar to the one shown on FIG. 11Cis constructed. The mashing analyzer is connected to a realistic sizemashing tank. Through a loop, the slurry liquid in the mashing unit iscontinuously pumped over the embedded ATR-IR spectroscopic unit in theanalyzer recording a spectra every minute. The ATR cell in the ATR-IRunit is tilted to an angle large enough to allow the sampling area to bedrained automatically by gravity during non-operation. The mashing tankis filled with water, the pH is adjusted by adding around 0.25 wt. %citric acid and the water is then heated to 57° C. The malted barley isadded to the heated water in the mashing tank under mechanical stirring.The IR spectra are analyzed in real time according to the methodologyand calibrations described in previous examples. From the analysis ADPand total sugar in Brix are given in real time as shown on FIG. 35,where the triangles show the total carbohydrates developed duringmashing obtained from the IR spectra (scale on the right-hand side) andthe circles show the ADP value based on analysis of the IR bands (scaleon the left-hand side). The dotted line illustrates the temperatureprofile used in the mashing.

REFERENCES

-   -   100 mashing unit    -   102 solution/mash    -   104 stirring unit    -   106 waveguide/optical connection path    -   108 side channel in the mashing tank    -   110 computer    -   112 display    -   114 optical probe    -   116 fiber connected to the optical probe    -   118 hole in the tank    -   120 extraction and/or recirculation valve    -   122 waste tank    -   124 pump unit (optional)    -   126 extraction probe    -   127 recirculation probe    -   128 inlet    -   130 outlet    -   132 filter    -   134 part of the extraction probe    -   136 vessel mount    -   200 IR spectrometer    -   202 box    -   203 ATR-IR unit    -   204 incoming IR light    -   206 optical component/mirror    -   207 crystal in a ATR-IR plate    -   208 clamp    -   210 back reflected IR light    -   212 O-ring    -   300 analyzing chamber/ATR-IR cell    -   302 connector inlet    -   304 connector outlet    -   400 spectroscopic unit    -   401 sealed mount unit    -   402 spectroscopic enclosure    -   403 ATR-IR unit    -   404 plate    -   406 spectrophotometer    -   407 crystal    -   409 display    -   408 computer    -   410 antenna    -   411 lid    -   412 tank mount    -   414 seal    -   416 clamp    -   500 pumping unit    -   501 housing    -   502 motor    -   504 pump head    -   506 flow cell    -   600 filtration unitli    -   602 hose

We claim:
 1. A system for controlling an enzymatic pre-treatmentprocess, the system comprising: an analyzing unit configured to beconnected in fluid communication, on-line and/or in-line with acontainer, and connected to an IR spectrometer, the analyzing unitconfigured for bringing a sample mixture into direct contact with the IRspectrometer for measuring attenuated total reflectance (ATR) IR spectraof a liquid part (solutes and solvents) of the sample mixture during anenzymatic pre-treatment process, and a calculation unit connected withthe IR spectrometer, the calculation unit being adapted for calculating:a ratio between specific species in the sample mixture based on the IRspectra of the sample mixture, and/or a concentration of one or morespecific species based on the IR spectra of the sample mixture, and/or adegree of polymerization of one or more of specific species based on theIR spectra of the sample mixture.
 2. A system according to claim 1wherein the analyzing unit is an ATR-IR cell adapted for containing asmall part of the sample mixture during measurements of ATR-IR spectraof the liquid part of the sample mixture during the enzymaticpre-treatment process, wherein the ATR-IR cell is mounted sealed to anATR-IR plate comprising a crystal, the ATR-IR plate being part of anATR-IR spectrometer.
 3. A system according to claim 2 further comprisesa connection unit connecting the analyzing unit with a containerconfigured for holding an enzymatic pre-treatment process, theconnection unit being adapted for guiding the small part of the samplemixture from the container and to the analyzing unit whereby ATR-IRspectra of the liquid part of the sample mixture is measured by theATR-IR spectrometer.
 4. A system according to claim 2, furthercomprising a container configured for holding an enzymatic pre-treatmentprocess; and an extraction probe that protrudes inside the container,the probe configured for extracting the small part of the sample mixturefor the ATR-IR spectra measurement at a user-determined position insidethe container.
 5. A system according to claim 1 wherein the analyzingunit is a spectroscopic unit comprising an ART-IR unit, aspectrophotometer for measuring IR spectra, and a computer, wherein theART-IR unit comprises an ATR-IR plate with a crystal.
 6. A systemaccording to claim 5 wherein the spectroscopic unit is attached directlyto a wall of a container or is attached to a wall of a container byconnecting means.
 7. A system according to claim 6, wherein the ATRplate can be turned up to 90 degrees around its own axis.
 8. A systemaccording to claim 1, further comprising the analyzing unit beingconnected in fluid communication, on-line and/or in-line with acontainer configured for holding an enzymatic pre-treatment process,where the container comprises a tank, a pipe, and/or a vessel.
 9. Asystem according to claim 1, further comprising the analyzing unit beingconnected in fluid communication, on-line and/or in-line with acontainer configured for holding an enzymatic pre-treatment process,wherein the container contains material to be enzymatically pre-treated.10. A system according to claim 9, wherein the material to beenzymatically pre-treated further comprises one or more enzymes added tothe sample.
 11. A system according to claim 9, where the containercomprises a tank, a pipe, and/or a vessel.
 12. A method of using thesystem of claim 1, said method being adapted for controlling anenzymatic pre-treatment process, wherein the method comprises steps of:a) providing a sample into a container configured for holding anenzymatic pre-treatment process and disposed in fluid communication withthe analyzing unit, where the sample includes one or more enzymes ordoes not include one or more enzymes; b) obtaining a sample mixture andperforming the enzymatic pre-treatment process by: adding one or moreenzymes to the sample if the sample does not contain one or more enzymesalready, or adding one or more enzymes to the sample already containingone or more enzymes; c) continuously exposing a portion of the samplemixture to the infrared (IR) spectrometer; d) continuously measuringattenuated total reflectance (ATR) IR spectra of the sample mixture withthe IR spectrometer in real time at wavenumbers between 400-3500 cm⁻¹during the enzymatic pre-treatment process, and e) feeding the measuredIR spectra to a calculating unit which unit: calculates informationrelating to specific species present in the sample mixture during theenzymatic pre-treatment process based on the IR spectra, wherein theinformation relating to the specific species present in the samplemixture includes: a ratio between the different specific species, and/ora concentration of one or more of the specific species, and/or a degreeof polymerization of one or more of the specific species; and feeds theinformation relating to the specific species in the sample mixture backto a user and/or to a control system connected to the container.
 13. Amethod according to claim 12, further comprising a step of: f) stoppingthe enzymatic pre-treatment process when: a predetermined ratio betweenthe specific species in the sample mixture is obtained, and/or aconcentration of one or more of the specific species reached apredetermined level, and/or a degree of polymerization of one or more ofthe specific species reached a predetermined level.
 14. A methodaccording to claim 12 further comprising a step of stirring the samplemixture during at least part of the enzymatic pre-treatment process, astep wherein water is added to the sample mixture during the enzymaticpre-treatment process, or both.
 15. A method according to claim 12further comprising an action of increasing or decreasing the temperaturein the sample and/or the sample mixture: prior to starting the enzymaticpre-treatment process, and/or during the enzymatic pre-treatmentprocess, and/or in an amount sufficient to stop the enzymaticpre-treatment process.
 16. A method according to claim 12, wherein thecontainer includes a tank, and the enzymatic pre-treatment process isstopped by one or more of: the system opening the tank automatically;removing the sample mixture from the tank, or increasing or decreasing atemperature in the tank.
 17. A method according to claim 12 wherein thesample is selected from naturally occurring carbohydrates or from cropscontaining disaccharide and polysaccharide.
 18. A method according toclaim 12 wherein the enzymatic pre-treatment process is a mashingprocess conducted prior to a fermentation process.
 19. A methodaccording to claim 12 wherein multiple enzymes are added to the samplemixture either at the same time or at different times and wherein atemperature of the sample mixture is adjusted during the pre-treatmentprocess to account for differences in temperature at which each of theenzymes is most active.
 20. A method according to claim 12 wherein theIR spectra are measured at wavenumbers between 400-3000 cm⁻¹, between400-2000 cm⁻¹, between 500-1500 cm⁻¹, between 700-1400 cm⁻¹, or between800-1300 cm⁻¹.